Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
#data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7fd11ec2b390>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7fd11eb2c3c8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    inputs_lr = tf.placeholder(tf.float32, name='input_lr')

    return inputs_real, inputs_z, inputs_lr


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    alpha = 0.2
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x[1 or 3]
        
        x1 = tf.layers.conv2d(images, 128, 5, strides=1, padding='same')
        x1 = tf.maximum(alpha * x1, x1)
        # 28x28x128
        
        x2 = tf.layers.conv2d(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=True)
        x2 = tf.maximum(alpha * x2, x2)
        # 14x14x256
        
        x3 = tf.layers.conv2d(x2, 512, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=True)
        x3 = tf.maximum(alpha * x3, x3)
        # 7x7x512

        # Flatten it
        flat = tf.reshape(x3, (-1, 7*7*512))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    alpha = 0.2
    with tf.variable_scope('generator', reuse = not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.nn.relu(x1)
        # 7x7x256 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.nn.relu(x2)
        # 14x14x128 now
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 28x28x64
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=1, padding='same')
        # 28x28xout_channel_dim now
        
        out = 0.5 * tf.tanh(logits)
        
        return out    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake) * 0.9))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)

    return d_train_opt, g_train_opt    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [ ]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    #tf.reset_default_graph()

    data_channels = data_shape[3]
    
    print_every=100
    show_every=100
    
    input_real, input_z, _ = model_inputs(data_shape[1], data_shape[2], data_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate, beta1)   
    
    steps = 0
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z})
                
                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Processed {}: ".format(steps * batch_size),
                          "Discriminator Loss: {:.4f}, ".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 25, input_z, data_channels, data_image_mode)
                
                steps = steps + 1

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 100
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 4

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, 
          batch_size, 
          z_dim, 
          learning_rate, 
          beta1, 
          mnist_dataset.get_batches,
          mnist_dataset.shape, 
          mnist_dataset.image_mode)
Processed 0:  Discriminator Loss: 7.3271,  Generator Loss: 0.7335
Processed 10000:  Discriminator Loss: 1.0287,  Generator Loss: 1.6233
Processed 20000:  Discriminator Loss: 1.5548,  Generator Loss: 0.6321
Processed 30000:  Discriminator Loss: 1.5289,  Generator Loss: 0.7509
Processed 40000:  Discriminator Loss: 1.4093,  Generator Loss: 0.6409
Processed 50000:  Discriminator Loss: 1.3641,  Generator Loss: 0.5194
Processed 60000:  Discriminator Loss: 1.3883,  Generator Loss: 0.5168
Processed 70000:  Discriminator Loss: 2.2647,  Generator Loss: 1.8694
Processed 80000:  Discriminator Loss: 1.4736,  Generator Loss: 0.4397
Processed 90000:  Discriminator Loss: 1.6133,  Generator Loss: 0.4114
Processed 100000:  Discriminator Loss: 1.5691,  Generator Loss: 0.4242
Processed 110000:  Discriminator Loss: 1.2033,  Generator Loss: 0.7309
Processed 120000:  Discriminator Loss: 1.2894,  Generator Loss: 0.6774
Processed 130000:  Discriminator Loss: 1.3194,  Generator Loss: 0.5893
Processed 140000:  Discriminator Loss: 1.3200,  Generator Loss: 0.5531
Processed 150000:  Discriminator Loss: 1.1309,  Generator Loss: 0.8947
Processed 160000:  Discriminator Loss: 1.2196,  Generator Loss: 0.8014
Processed 170000:  Discriminator Loss: 1.6640,  Generator Loss: 1.7024
Processed 180000:  Discriminator Loss: 1.6157,  Generator Loss: 0.4479
Processed 190000:  Discriminator Loss: 1.3042,  Generator Loss: 0.9177
Processed 200000:  Discriminator Loss: 1.3214,  Generator Loss: 0.6406
Processed 210000:  Discriminator Loss: 1.2548,  Generator Loss: 0.7498
Processed 220000:  Discriminator Loss: 1.5153,  Generator Loss: 0.5344
Processed 230000:  Discriminator Loss: 1.6293,  Generator Loss: 0.4703

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 100
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 10

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Processed 0:  Discriminator Loss: 3.5977,  Generator Loss: 0.3876
Processed 10000:  Discriminator Loss: 2.5053,  Generator Loss: 2.4697
Processed 20000:  Discriminator Loss: 0.8464,  Generator Loss: 2.3032
Processed 30000:  Discriminator Loss: 1.1383,  Generator Loss: 1.3432
Processed 40000:  Discriminator Loss: 1.4319,  Generator Loss: 0.9339
Processed 50000:  Discriminator Loss: 1.3477,  Generator Loss: 0.4866
Processed 60000:  Discriminator Loss: 0.4390,  Generator Loss: 1.4293
Processed 70000:  Discriminator Loss: 0.3295,  Generator Loss: 2.3935
Processed 80000:  Discriminator Loss: 1.0235,  Generator Loss: 2.3137
Processed 90000:  Discriminator Loss: 1.6317,  Generator Loss: 0.5235
Processed 100000:  Discriminator Loss: 0.9893,  Generator Loss: 0.6991
Processed 110000:  Discriminator Loss: 0.8772,  Generator Loss: 0.9078
Processed 120000:  Discriminator Loss: 1.2025,  Generator Loss: 3.5126
Processed 130000:  Discriminator Loss: 1.4726,  Generator Loss: 2.0056
Processed 140000:  Discriminator Loss: 1.1173,  Generator Loss: 1.5495
Processed 150000:  Discriminator Loss: 0.4118,  Generator Loss: 1.5720
Processed 160000:  Discriminator Loss: 0.5853,  Generator Loss: 1.3781
Processed 170000:  Discriminator Loss: 1.6221,  Generator Loss: 1.3967
Processed 180000:  Discriminator Loss: 0.6630,  Generator Loss: 1.0480
Processed 190000:  Discriminator Loss: 0.6349,  Generator Loss: 1.1695
Processed 200000:  Discriminator Loss: 2.2288,  Generator Loss: 1.7099
Processed 210000:  Discriminator Loss: 1.5308,  Generator Loss: 0.4695
Processed 220000:  Discriminator Loss: 0.6155,  Generator Loss: 1.6473
Processed 230000:  Discriminator Loss: 0.9586,  Generator Loss: 0.6705
Processed 240000:  Discriminator Loss: 0.7111,  Generator Loss: 1.0529
Processed 250000:  Discriminator Loss: 1.2739,  Generator Loss: 0.6347
Processed 260000:  Discriminator Loss: 0.7941,  Generator Loss: 1.0589
Processed 270000:  Discriminator Loss: 1.2848,  Generator Loss: 0.9269
Processed 280000:  Discriminator Loss: 0.6012,  Generator Loss: 1.0293
Processed 290000:  Discriminator Loss: 0.8132,  Generator Loss: 0.8562
Processed 300000:  Discriminator Loss: 0.0827,  Generator Loss: 5.4376
Processed 310000:  Discriminator Loss: 0.8271,  Generator Loss: 2.6660
Processed 320000:  Discriminator Loss: 0.8582,  Generator Loss: 0.9094
Processed 330000:  Discriminator Loss: 0.4277,  Generator Loss: 1.3658
Processed 340000:  Discriminator Loss: 1.4313,  Generator Loss: 0.5577
Processed 350000:  Discriminator Loss: 1.5546,  Generator Loss: 0.5224
Processed 360000:  Discriminator Loss: 1.4732,  Generator Loss: 0.4866
Processed 370000:  Discriminator Loss: 1.2709,  Generator Loss: 0.5750
Processed 380000:  Discriminator Loss: 0.0286,  Generator Loss: 3.5321
Processed 390000:  Discriminator Loss: 0.1320,  Generator Loss: 2.1886
Processed 400000:  Discriminator Loss: 1.8602,  Generator Loss: 0.4739
Processed 410000:  Discriminator Loss: 0.2412,  Generator Loss: 2.5331
Processed 420000:  Discriminator Loss: 1.1387,  Generator Loss: 0.7007
Processed 430000:  Discriminator Loss: 0.6565,  Generator Loss: 1.0220
Processed 440000:  Discriminator Loss: 0.3979,  Generator Loss: 4.1216
Processed 450000:  Discriminator Loss: 2.1603,  Generator Loss: 0.4185
Processed 460000:  Discriminator Loss: 0.3629,  Generator Loss: 2.5672
Processed 470000:  Discriminator Loss: 0.5017,  Generator Loss: 2.0012
Processed 480000:  Discriminator Loss: 1.5129,  Generator Loss: 0.4860
Processed 490000:  Discriminator Loss: 2.1975,  Generator Loss: 0.3723
Processed 500000:  Discriminator Loss: 0.5041,  Generator Loss: 4.9808
Processed 510000:  Discriminator Loss: 0.4472,  Generator Loss: 1.3005
Processed 520000:  Discriminator Loss: 0.7884,  Generator Loss: 0.8247
Processed 530000:  Discriminator Loss: 2.0415,  Generator Loss: 0.3636
Processed 540000:  Discriminator Loss: 0.6425,  Generator Loss: 1.0216
Processed 550000:  Discriminator Loss: 1.3027,  Generator Loss: 0.5694
Processed 560000:  Discriminator Loss: 0.3977,  Generator Loss: 2.1803
Processed 570000:  Discriminator Loss: 1.8942,  Generator Loss: 0.4086
Processed 580000:  Discriminator Loss: 1.3656,  Generator Loss: 0.5088
Processed 590000:  Discriminator Loss: 1.0583,  Generator Loss: 1.0507
Processed 600000:  Discriminator Loss: 0.4742,  Generator Loss: 1.3419
Processed 610000:  Discriminator Loss: 1.5396,  Generator Loss: 0.4559
Processed 620000:  Discriminator Loss: 0.0738,  Generator Loss: 2.9858
Processed 630000:  Discriminator Loss: 0.1421,  Generator Loss: 2.7201
Processed 640000:  Discriminator Loss: 2.6757,  Generator Loss: 0.4830
Processed 650000:  Discriminator Loss: 0.4449,  Generator Loss: 1.9164
Processed 660000:  Discriminator Loss: 0.8218,  Generator Loss: 1.0822
Processed 670000:  Discriminator Loss: 0.6186,  Generator Loss: 1.0886
Processed 680000:  Discriminator Loss: 1.0404,  Generator Loss: 0.6930
Processed 690000:  Discriminator Loss: 0.7692,  Generator Loss: 1.2060
Processed 700000:  Discriminator Loss: 0.4563,  Generator Loss: 2.2975
Processed 720000:  Discriminator Loss: 0.7820,  Generator Loss: 0.9517
Processed 730000:  Discriminator Loss: 0.4647,  Generator Loss: 3.1027
Processed 740000:  Discriminator Loss: 1.4878,  Generator Loss: 0.4914
Processed 750000:  Discriminator Loss: 2.2990,  Generator Loss: 0.3598
Processed 760000:  Discriminator Loss: 1.5132,  Generator Loss: 0.5171
Processed 770000:  Discriminator Loss: 1.1780,  Generator Loss: 1.2860
Processed 780000:  Discriminator Loss: 0.5299,  Generator Loss: 1.6264
Processed 790000:  Discriminator Loss: 1.0984,  Generator Loss: 0.6224
Processed 800000:  Discriminator Loss: 1.4644,  Generator Loss: 0.5214
Processed 810000:  Discriminator Loss: 1.5986,  Generator Loss: 1.5906
Processed 820000:  Discriminator Loss: 0.6818,  Generator Loss: 1.4748
Processed 830000:  Discriminator Loss: 1.7212,  Generator Loss: 0.5058
Processed 840000:  Discriminator Loss: 0.5547,  Generator Loss: 1.2363
Processed 850000:  Discriminator Loss: 0.5125,  Generator Loss: 3.9890
Processed 860000:  Discriminator Loss: 1.4239,  Generator Loss: 0.5526
Processed 870000:  Discriminator Loss: 0.8201,  Generator Loss: 1.0240
Processed 880000:  Discriminator Loss: 1.6016,  Generator Loss: 0.4875
Processed 890000:  Discriminator Loss: 1.1792,  Generator Loss: 0.7851
Processed 900000:  Discriminator Loss: 1.0908,  Generator Loss: 0.9097
Processed 910000:  Discriminator Loss: 1.0076,  Generator Loss: 0.7192
Processed 920000:  Discriminator Loss: 1.1323,  Generator Loss: 0.5931
Processed 930000:  Discriminator Loss: 1.3512,  Generator Loss: 0.5189
Processed 940000:  Discriminator Loss: 1.7943,  Generator Loss: 0.4002
Processed 950000:  Discriminator Loss: 0.6787,  Generator Loss: 1.8435
Processed 960000:  Discriminator Loss: 1.0931,  Generator Loss: 0.7558
Processed 970000:  Discriminator Loss: 1.2079,  Generator Loss: 1.4376
Processed 980000:  Discriminator Loss: 1.9518,  Generator Loss: 0.3855
Processed 990000:  Discriminator Loss: 0.1260,  Generator Loss: 2.7368
Processed 1000000:  Discriminator Loss: 1.0037,  Generator Loss: 0.7593
Processed 1010000:  Discriminator Loss: 1.5236,  Generator Loss: 0.4399
Processed 1020000:  Discriminator Loss: 1.6210,  Generator Loss: 0.4358
Processed 1030000:  Discriminator Loss: 1.3396,  Generator Loss: 1.1747
Processed 1040000:  Discriminator Loss: 2.0954,  Generator Loss: 0.3616
Processed 1050000:  Discriminator Loss: 0.8949,  Generator Loss: 0.8299
Processed 1060000:  Discriminator Loss: 0.0278,  Generator Loss: 3.8691
Processed 1070000:  Discriminator Loss: 0.1124,  Generator Loss: 2.6833
Processed 1080000:  Discriminator Loss: 0.8810,  Generator Loss: 1.6393
Processed 1090000:  Discriminator Loss: 1.7466,  Generator Loss: 0.4320
Processed 1100000:  Discriminator Loss: 1.4310,  Generator Loss: 0.5299
Processed 1110000:  Discriminator Loss: 0.9509,  Generator Loss: 0.9136
Processed 1120000:  Discriminator Loss: 1.8828,  Generator Loss: 0.4069
Processed 1130000:  Discriminator Loss: 1.5372,  Generator Loss: 0.4957
Processed 1140000:  Discriminator Loss: 1.0352,  Generator Loss: 0.8920
Processed 1150000:  Discriminator Loss: 1.3017,  Generator Loss: 0.5708
Processed 1160000:  Discriminator Loss: 1.6654,  Generator Loss: 0.4261
Processed 1170000:  Discriminator Loss: 0.6332,  Generator Loss: 1.3599
Processed 1180000:  Discriminator Loss: 1.6108,  Generator Loss: 0.4514
Processed 1190000:  Discriminator Loss: 0.8526,  Generator Loss: 0.8861
Processed 1200000:  Discriminator Loss: 1.5157,  Generator Loss: 0.4912
Processed 1210000:  Discriminator Loss: 1.3556,  Generator Loss: 0.4926
Processed 1220000:  Discriminator Loss: 0.7840,  Generator Loss: 0.9381
Processed 1230000:  Discriminator Loss: 1.7424,  Generator Loss: 0.4354
Processed 1240000:  Discriminator Loss: 1.7970,  Generator Loss: 0.4342
Processed 1250000:  Discriminator Loss: 2.1501,  Generator Loss: 0.3799
Processed 1260000:  Discriminator Loss: 1.5198,  Generator Loss: 0.4937
Processed 1270000:  Discriminator Loss: 0.3689,  Generator Loss: 2.3222
Processed 1280000:  Discriminator Loss: 1.2764,  Generator Loss: 0.6905
Processed 1290000:  Discriminator Loss: 1.4916,  Generator Loss: 0.5881
Processed 1300000:  Discriminator Loss: 1.7522,  Generator Loss: 0.4548
Processed 1310000:  Discriminator Loss: 0.8490,  Generator Loss: 1.1271
Processed 1320000:  Discriminator Loss: 1.0585,  Generator Loss: 0.6857
Processed 1330000:  Discriminator Loss: 0.0677,  Generator Loss: 8.8853
Processed 1340000:  Discriminator Loss: 0.0910,  Generator Loss: 2.7149
Processed 1350000:  Discriminator Loss: 0.9204,  Generator Loss: 1.5562
Processed 1360000:  Discriminator Loss: 1.4650,  Generator Loss: 0.5057
Processed 1370000:  Discriminator Loss: 1.3495,  Generator Loss: 0.5964
Processed 1380000:  Discriminator Loss: 1.6975,  Generator Loss: 0.4385
Processed 1390000:  Discriminator Loss: 1.2670,  Generator Loss: 2.8594
Processed 1400000:  Discriminator Loss: 1.1706,  Generator Loss: 0.7527
Processed 1410000:  Discriminator Loss: 0.7122,  Generator Loss: 1.0937
Processed 1420000:  Discriminator Loss: 1.3126,  Generator Loss: 0.6833
Processed 1430000:  Discriminator Loss: 1.0641,  Generator Loss: 0.7365

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

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